Abstract P397: Living in Ethnic-enclave Neighborhoods May Attenuate the Negative Effect of Acculturation on Blood Pressure in Refugees and Maintain Immigrant Health Effect
Bibliographic record
Abstract
Background: Researchers have long hypothesized that acculturation (duration of U.S. residence [DOR]) is associated with increases in blood pressure in immigrant populations. However, little is known about the potential effects of acculturation on hypertension (HTN) in refugees living in ethnic-enclave neighborhoods. We conducted a cross-sectional study of Somali refugees and immigrants (a largely enclaved population) living in Minneapolis-St. Paul, Minnesota. Method: We analyzed data from 1155 Somali refugees/immigrants who arrived in United States from 1990 to 2015 and are participating in an ongoing study of Somali refugees in the Twin Cities. Using 10 years as a threshold, the study population was divided into two groups based on DOR: short (< 10 years) or long (>= 10 years). Main outcome was HTN, which was defined using the protocol of international HTN studies. Differences between the two groups were determined by chi-squared test and t-test. Multivariate logistic regression was used to determine association between HTN and DOR. Results: Of 1155 subjects, 572 (49.6%) had DOR >= 10 years. Mean DOR was 9.8 ± 6.0 years (median of 10), and 58.1% (559 of 1155) were women. The short DOR group was younger (mean age 44.6 ± 18.0 vs 51.4 ± 18.1 years, P< 0.001. There was no difference in mean systolic blood pressure (SBP) between groups (117 mmHg vs 119 mmHg, P=0.087) and no correlation between SBP and DOR (linear R 2 =0.012). Prevalence of HTN was 29.5% (341 of 1155), with 179 of these 341 (52.5%) having uncontrolled HTN. HTN increased with aging: 4.1% (19 of 457) for age group 18-39, 15.3% (47 of 306) for ages 40-59, and 29.7% (114 of 384) for ages 60 + . After adjusting for body mass index, marital status, English language (written and spoken), health insurance, income / poverty ratio, language spoken at home, employment, education and DOR, age was the only predicator of HTN. Even after matching two groups by age, the distribution of HTN was similar between the two age-matched DOR groups (P=0.1879). Conclusions: DOR (acculturation) was not a predictor of HTN in this highly enclaved refugee/immigrant sample. Further studies are needed to identify which cultural and neighborhood environment factors may operate to blunt the effects of acculturation on blood pressure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".